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KDD
2008
ACM
137views Data Mining» more  KDD 2008»
14 years 8 months ago
Learning classifiers from only positive and unlabeled data
The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and ...
Charles Elkan, Keith Noto
STOC
2003
ACM
124views Algorithms» more  STOC 2003»
14 years 8 months ago
Extractors: optimal up to constant factors
This paper provides the first explicit construction of extractors which are simultaneously optimal up to constant factors in both seed length and output length. More precisely, fo...
Chi-Jen Lu, Omer Reingold, Salil P. Vadhan, Avi Wi...
EDBT
2008
ACM
111views Database» more  EDBT 2008»
14 years 7 months ago
Micro-aggregation-based heuristics for p-sensitive k-anonymity: one step beyond
Micro-data protection is a hot topic in the field of Statistical Disclosure Control (SDC), that has gained special interest after the disclosure of 658000 queries by the AOL searc...
Agusti Solanas, Francesc Sebé, Josep Doming...
WSDM
2010
ACM
213views Data Mining» more  WSDM 2010»
14 years 5 months ago
Corroborating Information from Disagreeing Views
We consider a set of views stating possibly conflicting facts. Negative facts in the views may come, e.g., from functional dependencies in the underlying database schema. We want ...
Alban Galland, Serge Abiteboul, Amélie Mari...
WSDM
2010
ACM
245views Data Mining» more  WSDM 2010»
14 years 5 months ago
Improving Quality of Training Data for Learning to Rank Using Click-Through Data
In information retrieval, relevance of documents with respect to queries is usually judged by humans, and used in evaluation and/or learning of ranking functions. Previous work ha...
Jingfang Xu, Chuanliang Chen, Gu Xu, Hang Li, Elbi...